'''simple docstring'''






# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


_UpperCAmelCase    :     Dict         =       '''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Tuple=None				):
     if subparsers is not None:
          _A    		=  subparsers.add_parser('tpu-config'   ,		description=_description				)
     else:
          _A    		=  argparse.ArgumentParser('Accelerate tpu-config command'   ,		description=_description				)
     # Core arguments
     _A    		=  parser.add_argument_group(
         'Config Arguments'   ,		'Arguments that can be configured through `accelerate config`.'				)
     config_args.add_argument(
         '--config_file'   ,		type=__snake_case   ,		default=__snake_case   ,		help='Path to the config file to use for accelerate.'   ,		)
     config_args.add_argument(
         '--tpu_name'   ,		default=__snake_case   ,		help='The name of the TPU to use. If not specified, will use the TPU specified in the config file.'   ,		)
     config_args.add_argument(
         '--tpu_zone'   ,		default=__snake_case   ,		help='The zone of the TPU to use. If not specified, will use the zone specified in the config file.'   ,		)
     _A    		=  parser.add_argument_group('TPU Arguments'   ,		'Arguments for options ran inside the TPU.'				)
     pod_args.add_argument(
         '--use_alpha'   ,		action='store_true'   ,		help='Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.'   ,		)
     pod_args.add_argument(
         '--command_file'   ,		default=__snake_case   ,		help='The path to the file containing the commands to run on the pod on startup.'   ,		)
     pod_args.add_argument(
         '--command'   ,		action='append'   ,		nargs='+'   ,		help='A command to run on the pod. Can be passed multiple times.'   ,		)
     pod_args.add_argument(
         '--install_accelerate'   ,		action='store_true'   ,		help='Whether to install accelerate on the pod. Defaults to False.'   ,		)
     pod_args.add_argument(
         '--accelerate_version'   ,		default='latest'   ,		help='The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.'   ,		)
     pod_args.add_argument(
         '--debug'   ,		action='store_true'   ,		help='If set, will print the command that would be run instead of running it.'				)

     if subparsers is not None:
          parser.set_defaults(func=__snake_case				)
     return parser





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Union[str, Any]				):
     _A    		=  None

     # Get the default from the config file if it exists.
     if args.config_file is not None or os.path.isfile(__snake_case				):
          _A    		=  load_config_from_file(args.config_file				)
          if not args.command_file and defaults.command_file is not None and not args.command:
               _A    		=  defaults.command_file
          if not args.command and defaults.commands is not None:
               _A    		=  defaults.commands
          if not args.tpu_name:
               _A    		=  defaults.tpu_name
          if not args.tpu_zone:
               _A    		=  defaults.tpu_zone
     if args.accelerate_version == "dev":
          _A    		=  'git+https://github.com/huggingface/accelerate.git'
     elif args.accelerate_version == "latest":
          _A    		=  'accelerate -U'
     elif isinstance(parse(args.accelerate_version				)   ,		__snake_case				):
          _A    		=  F'accelerate=={args.accelerate_version}'

     if not args.command_file and not args.command:
          raise ValueError('You must specify either a command file or a command to run on the pod.'				)

     if args.command_file:
          with open(args.command_file   ,		'r'				) as f:
               _A    		=  [f.read().splitlines()]

    # To turn list of lists into list of strings
     if isinstance(args.command[0]   ,		__snake_case				):
          _A    		=  [line for cmd in args.command for line in cmd]
     # Default to the shared folder and install accelerate
     _A    		=  ['cd /usr/share']
     if args.install_accelerate:
          new_cmd += [F'pip install {args.accelerate_version}']
     new_cmd += args.command
     _A    		=  '; '.join(__snake_case				)

     # Then send it to gcloud
     # Eventually try to use google-api-core to do this instead of subprocess
     _A    		=  ['gcloud']
     if args.use_alpha:
          cmd += ["alpha"]
     cmd += [
         "compute",
         "tpus",
         "tpu-vm",
         "ssh",
         args.tpu_name,
         "--zone",
         args.tpu_zone,
         "--command",
         args.command,
         "--worker",
         "all",
     ]
     if args.debug:
          print(F'Running {" ".join(__snake_case				)}'				)
          return
     subprocess.run(__snake_case				)
     print('Successfully setup pod.'				)





def  _SCREAMING_SNAKE_CASE		(      ):
     _A    		=  tpu_command_parser()
     _A    		=  parser.parse_args()

     tpu_command_launcher(__snake_case				)







"""simple docstring"""



from typing import Optional, Tuple

import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def      UpperCAmelCase     (       _lowercase :   str			,   _lowercase :   int			,   _lowercase :   Optional[int]=1E-12	)						-> List[str]:


	"""simple docstring"""




	lowerCAmelCase_      							=			jnp.divide(emb_a.T			,   jnp.clip(jnp.linalg.norm(_lowercase			,   axis=1	)			,   a_min=_lowercase	)	).T
	lowerCAmelCase_      							=			jnp.divide(emb_a.T			,   jnp.clip(jnp.linalg.norm(_lowercase			,   axis=1	)			,   a_min=_lowercase	)	).T
	return jnp.matmul(_lowercase			,   norm_emb_a.T	)







class    __a						( nn.Module							):
				lowerCamelCase     :       CLIPConfig
				lowerCamelCase     :       jnp.dtype			       =jnp.floataa







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			FlaxCLIPVisionModule(self.config.vision_config   )
					lowerCAmelCase_      							=			nn.Dense(self.config.projection_dim      ,	use_bias=UpperCAmelCase      ,	dtype=self.dtype   )

					lowerCAmelCase_      							=			self.param('''concept_embeds'''      ,	jax.nn.initializers.ones      ,	(17, self.config.projection_dim)   )
					lowerCAmelCase_      							=			self.param(
					    '''special_care_embeds'''      ,	jax.nn.initializers.ones      ,	(3, self.config.projection_dim)   )

					lowerCAmelCase_      							=			self.param('''concept_embeds_weights'''      ,	jax.nn.initializers.ones      ,	(17,)   )
					lowerCAmelCase_      							=			self.param('''special_care_embeds_weights'''      ,	jax.nn.initializers.ones      ,	(3,)   )







				def __call__( self      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.vision_model(UpperCAmelCase   )[1]
					lowerCAmelCase_      							=			self.visual_projection(UpperCAmelCase   )

					lowerCAmelCase_      							=			jax_cosine_distance(UpperCAmelCase      ,	self.special_care_embeds   )
					lowerCAmelCase_      							=			jax_cosine_distance(UpperCAmelCase      ,	self.concept_embeds   )

					# increase this value to create a stronger `nfsw` filter
					# at the cost of increasing the possibility of filtering benign image inputs
					lowerCAmelCase_      							=			0.0

					lowerCAmelCase_      							=			special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
					lowerCAmelCase_      							=			jnp.round(UpperCAmelCase      ,	3   )
					lowerCAmelCase_      							=			jnp.any(special_scores > 0      ,	axis=1      ,	keepdims=UpperCAmelCase   )
					# Use a lower threshold if an image has any special care concept
					lowerCAmelCase_      							=			is_special_care * 0.0_1

					lowerCAmelCase_      							=			cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
					lowerCAmelCase_      							=			jnp.round(UpperCAmelCase      ,	3   )
					lowerCAmelCase_      							=			jnp.any(concept_scores > 0      ,	axis=1   )

					return has_nsfw_concepts







class    __a						( __snake_case							):
				lowerCamelCase     :       Optional[Any]			       =CLIPConfig
				lowerCamelCase     :       List[str]			       ='clip_input'
				lowerCamelCase     :       int			       =FlaxStableDiffusionSafetyCheckerModule







				def __init__( self      ,	UpperCAmelCase      ,	UpperCAmelCase = None      ,	UpperCAmelCase = 0      ,	UpperCAmelCase = jnp.floataa      ,	UpperCAmelCase = True      ,	**UpperCAmelCase      ,	):

					'''simple docstring'''






					if input_shape is None:
						lowerCAmelCase_      							=			(1, 224, 224, 3)
					lowerCAmelCase_      							=			self.module_class(config=UpperCAmelCase      ,	dtype=UpperCAmelCase      ,	**UpperCAmelCase   )
					super().__init__(UpperCAmelCase      ,	UpperCAmelCase      ,	input_shape=UpperCAmelCase      ,	seed=UpperCAmelCase      ,	dtype=UpperCAmelCase      ,	_do_init=_do_init   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase = None   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			jax.random.normal(UpperCAmelCase      ,	UpperCAmelCase   )

					lowerCAmelCase_							,	lowerCAmelCase_      							=			jax.random.split(UpperCAmelCase   )
					lowerCAmelCase_      							=			{'''params''': params_rng, '''dropout''': dropout_rng}

					lowerCAmelCase_      							=			self.module.init(UpperCAmelCase      ,	UpperCAmelCase   )['''params''']

					return random_params







				def __call__( self      ,	UpperCAmelCase      ,	UpperCAmelCase = None      ,	):

					'''simple docstring'''






					lowerCAmelCase_      							=			jnp.transpose(UpperCAmelCase      ,	(0, 2, 3, 1)   )

					return self.module.apply(
					    {'''params''': params or self.params}      ,	jnp.array(UpperCAmelCase      ,	dtype=jnp.floataa   )      ,	rngs={}      ,	)